In this study, the response surface method and experimental design were applied as an alternative to conventional methods for the optimization of coagulation tests. A central composite design, with 4 axial points, 4 factorial points and 5 replicates at the center point were used to build a model for predicting and optimizing the coagulation process. Mathematical model equations were derived by computer simulation programming with a least squares method using the Minitab 15 software. In these equations, the removal efficiencies of turbidity and total organic carbon (TOC) were expressed as second-order functions of two factors, such as alum dose and coagulation pH. Statistical checks (ANOVA table, and value, model lack of fit test, and p value) indicated that the model was adequate for representing the experimental data. The p values showed that the quadratic effects of alum dose and coagulation pH were highly significant. In other words, these two factors had an important impact on the turbidity and TOC of treated water. To gain a better understanding of the two variables for optimal coagulation performance, the model was presented as both 3-D response surface and 2-D contour graphs. As a compromise for the simultaneously removal of maximum amounts of 92.5% turbidity and 39.5% TOC, the optimum conditions were found with 44 mg/L alum at pH 7.6. The predicted response from the model showed close agreement with the experimental data ( values of 90.63% and 91.43% for turbidity removal and TOC removal, respectively), which demonstrates the effectiveness of this approach in achieving good predictions, while minimizing the number of experiments required.

Khuri AI. An overview of the use of generalized linear models in response surface methodology. Nonlinear Anal. 2001;47:2023-2034.

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